Image Classification of Vegetable Quality using Support Vector Machine based on Convolutional Neural Network

In the realm of computer vision, image classification is a critical issue with many applications, including multimedia content analysis, security and surveillance, and medical imaging. The accuracy of image classification algorithms has considerably increased with the development of deep learning. The discipline of image classification has dramatically benefited from the usage of pre-trained Convolutional Neural Network (CNN) models. In this study, we perform image classification on the Wang dataset composed of 1000 images separated into ten categories, using six different pre-trained CNN models. The research made use of pre-trained versions of VGG16, Densenet, Mobilenet, Inception V3, Resnet50, and Xception models. We assessed the model performances in terms of training and testing accuracy. Testing accuracies for ten unique batches of data were calculated and averaged out. Densenet outperformed state-of-the-art models like Xception by a small margin, mainly due to low data availability. The findings of this study show how pre-trained models have advanced the field of image classification and shed light on how well these models perform in diverse image classification tasks. This study can serve as a valuable reference for researchers and practitioners in the field of computer vision and deep learning and help inform their choices when selecting pre-trained models for image classification, depending on their needs, while also considering data availability and computational constraints.

The sorting process of glass recycling is crucial to determine the grade of glass cullet. Hence, this paper aims to develop an algorithm for the classification of contaminants in glass recycling, presenting a hybrid model integrating two remarkable classifiers: Convolutional Neural Network (CNN) and Support Vector Machines (SVM), which have been proven to achieve the state-of-the-art results in solving classification problem. The proposed approach utilizes pre-trained CNN model as automatic feature extractor and SVM as an output predictor. The hybrid model extracts feature of contaminants from raw images and predicts the corresponding class. A contaminant database consisting around 2000 images is collected with a set of constraints to best portray the realism in practical environment. The performance of three different pre-trained CNN models (i.e. AlexNet, VGG-16 and GoogLeNet) in the hybrid model is tested and evaluated using comparative analysis. Since there is no similar approach used in this research field of interest in the past, the performance of the hybrid models is compared to the fine-tuned CNN models in the aspect of classification accuracy. In this study, the integration of VGG-16 and SVM was improved by 0.17% with SVM as classifier and 0.50% with data augmentation, achieving the highest classification accuracy of 100% among the other models. Among the factors that cause the VGG-16+SVM model with data augmentation to outperform are the depth of network, input size, stride size, size of filters and total number of weights and MACs. The proposed method is more accurate and efficient as compared to current existing method, producing high grade glass cullet at low cost.

Emotions expressed by humans can be identified from facial expressions, speech signals, or physiological signals. Among them, the use of physiological signals for emotion classification is a notable emerging area of research. In emotion recognition, a person’s electrocardiogram (ECG) and galvanic skin response (GSR) signals cannot be manipulated, unlike facial and voice signals. Moreover, wearables such as smartwatches and wristbands enable the detection of emotions in people’s naturalistic environment. During the COVID-19 pandemic, it was necessary to detect people’s emotions in order to ensure that appropriate actions were taken according to the prevailing situation and achieve societal balance. Experimentally, the duration of the emotion stimulus period and the social and non-social contexts of participants influence the emotion classification process. Hence, classification of emotions when participants are exposed to the elicitation process for a longer duration and taking into consideration the social context needs to be explored. This work explores the classification of emotions using five pretrained convolutional neural network (CNN) models: MobileNet, NASNetMobile, DenseNet 201, InceptionResnetV2, and EfficientNetB7. The continuous wavelet transform (CWT) coefficients were detected from ECG and GSR recordings from the AMIGOS database with suitable filtering. Scalograms of the sum of frequency coefficients versus time were obtained and converted into images. Emotions were classified using the pre-trained CNN models. The valence and arousal emotion classification accuracy obtained using ECG and GSR data were, respectively, 91.27% and 91.45% using the InceptionResnetV2 CNN classifier and 99.19% and 98.39% using the MobileNet CNN classifier. Other studies have not explored the use of scalograms to represent ECG and GSR CWT features for emotion classification using deep learning models. Additionally, this study provides a novel classification of emotions built on individual and group settings using ECG data. When the participants watched long-duration emotion elicitation videos individually and in groups, the accuracy was around 99.8%. MobileNet had the highest accuracy and shortest execution time. These subject-independent classification methods enable emotion classification independent of varying human behavior.

Cervical cancer is one of the fastest growing global health problems and leading cause of mortality among women of developing countries. Automated Pap smear cell recognition and classification in early stage of cell development is crucial for effective disease diagnosis and immediate treatment. Thus, in this article, we proposed a novel internet of health things (IoHT)-driven deep learning framework for detection and classification of cervical cancer in Pap smear images using concept of transfer learning. Following transfer learning, convolutional neural network (CNN) was combined with different conventional machine learning techniques like K nearest neighbor, naive Bayes, logistic regression, random forest and support vector machines. In the proposed framework, feature extraction from cervical images is performed using pre-trained CNN models like InceptionV3, VGG19, SqueezeNet and ResNet50, which are fed into dense and flattened layer for normal and abnormal cervical cells classification. The performance of the proposed IoHT frameworks is evaluated using standard Pap smear Herlev dataset. The proposed approach was validated by analyzing precision, recall, F1-score, training–testing time and support parameters. The obtained results concluded that CNN pre-trained model ResNet50 achieved the higher classification rate of 97.89% with the involvement of random forest classifier for effective and reliable disease detection and classification. The minimum training time and testing time required to train model were 0.032 s and 0.006 s, respectively.

Scene classification of high resolution remote sensing images plays an important role for a wide range of applications. While significant efforts have been made in developing various methods for scene classification, most of them are based on handcrafted or shallow learning-based features. In this paper, we investigate the use of deep convolutional neural network (CNN) for scene classification. To this end, we first adopt two simple and effective strategies to extract CNN features: (1) using pre-trained CNN models as universal feature extractors, and (2) domain-specifically fine-tuning pre-trained CNN models on our scene classification dataset. Then, scene classification is carried out by using simple classifiers such as linear support vector machine (SVM). In our work, three off-the-shelf CNN models including AlexNet [1], VGGNet [2], and GoogleNet [3] are investigated. Comprehensive evaluations on a publicly available 21 classes land use dataset and comparisons with several state-of-the-art approaches demonstrate that deep CNN features are effective for scene classification of high resolution remote sensing images.

The latest advances of deep learning and particularly convolutional neural networks (CNNs) have proven more than once their high accuracy in disease detection. In this chapter, we propose a new deep learning-based approach for COVID-19 detection from chest X-ray images. The proposed approach applies, in an efficient way, the techniques of transfer learning and fine-tuning from pre-trained CNN models (InceptionV3, VGG16, MobileNet, EfficientNet, etc.). The dataset used for our experiments has three classes: normal, COVID-19, and other pathologies. The dataset is split into three sub-sets as follows: 70% for training, 15% for validation, and 15% for the final test. To avoid underfitting or overfitting problems during the training process, we apply regularization techniques (L1 & L2 regularizations, dropout, data augmentation, early stopping, cross-validation, etc.), which help in learning and providing a generalizable solution. As a result, we demonstrate the high efficiency of the proposed CNNs for the detection of COVID-19 from chest X-ray images. A comparison of different architectures shows that VGG16 and MobileNet provide the highest scores: 97.5% and 99.3% of accuracy respectively, 98.7% and 99.3% of sensitivity respectively. In addition, both models provide the scores of 96.3% and 99.2% respectively for specificity. The proposed solution is deployed in the cloud to provide high availability in real time, thanks to a responsive website, and this without the need to download, install, and configure the required libraries.

Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN InceptionV3 model and combine it with three data augmentations to recognize up to 100 butterfly species. To curb overfitting, this study employs a series of data augmentation techniques. In parallel, we refine the InceptionV3 model by reducing the number of layers and integrating four new layers. The test results demonstrate that our proposed model achieves an impressive accuracy of 99.43% for 15 classes with only 10 epochs, exceeding prior models by approximately 5%. When extended to 100 classes, the model maintains a high accuracy rate of 98.49% with 50 epochs. The proposed model surpasses the performance of standard pre-trained models, including VGG16, ResNet50, and InceptionV3, illustrating its potential for broader application.

Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework

Multi-grade brain tumor classification using deep CNN with extensive data augmentation

<p class="Normal1">Medicinal plants have been found and utilized in traditional medical practices from ancient times. Many medicinal plants play a vital role in curating many life threatening diseases. Very few of the medicinal herbs are commercially cultivated. Many plant diseases are there which destroys these medicinal plants. Early detection of plant diseases can prevent the huge loss of these medicinal plants. Here, we presented a hybrid model that makes use of SVM along with the traditional convolutional neural network (CNN) for predicting <em>Basil</em> plants leaves diseases. We transformed the conventional CNN model by adding a classification layer Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) after feature extraction and this approach tends to perform better than traditional CNN as we make the dataset balanced by data augmentation and SVN and KNN tend to perform better in case of balanced samples. CNN is used for training, SVM/KNN is used for classification. The advantages of CNN and SVM are used in proposed the CNN and SVM and KNN model. It is assumed that such a combined model would incorporate the benefits of CNN and SVM. Here, we identified the four types of diseases that affect <em>basil</em> plant leaves as Leaf spot, Downy mildew, Fusarium wilt, Fungal, and Healthy. Since there isn’t a standard dataset for <em>basil</em> leaves, we created our own 803 picture data set and used various machine learning techniques to train and evaluate the model. However, over other existing algorithms, our hybrid model i.e., CNN+SVM has produced more accurate results. For five classes of <em>basil</em> plant leaves, the proposed model produced 95.02% accuracy of for leaf diseases.</p>

Introduction: A form of malignant tumour known as lymphoma originated in lymphoid hematopoietic organs. Because the physical characteristics of the many lymphoma classes are similar, accurately diagnosing lymphomas is one of the most difficult tasks. Hence, an efficient classification of lymphoma plays a very important role in order to provide patients with prompt care. The purpose of this work is to evaluate the performance of pre-trained Convolutional Neural Networks (CNNs) in the multiclass categorization of lymphomas. Objectives: Classification of Non-Hodgkin lymphomas by adopting pre-trained CNN architectures like ResNet50, VGG16, InceptionV3 and DenseNet201 are adopted. Utilize several pre-processing techniques for denoising, rescaling, and enriching the input images, including gaussian filter, min-max normalisation, and data augmentation. Perform a detailed performance analysis of the proposed work with existing models. Methods: This research uses the different CNN architectures such as VGG16, DenseNet201, InceptionV3 to classify the lymphoma. In pre-processing, the gaussian filter is used to denoise and smoothen the images, min-max normalization is used to rescale the images and the data augmentation is used for solving the data imbalance issue. Transfer Learning and Fine-Tuning is done which improves the overall performance of the model. Results: This study makes use of the multi cancer dataset from Kaggle. The performance of these pre-trained CNN models is evaluated using accuracy, precision, recall, and the F-measure. Based on simulation findings, DenseNet201 outperforms VGG16 and InceptionV3 with an accuracy of 99.90%. Furthermore, FFNN-ResNet50 and HPC are two current studies that are used to compare ResNet50 and DenseNet201. ResNet50-DenseNet201 has a high accuracy of 99.90% compared to FFNN-ResNet50 and HPC.   Conclusions: Several CNN architectures, including VGG16, InceptionV3 and DenseNet201 are employed in this study to categorize lymphomas. Several NHL classifications, including FL, CLL, and MCL, are classified using the pre-trained CNN architecture. The gaussian filter, which aids in smoothing the pictures, is used to eliminate noise from the histopathology images. The pixel limits are then scaled using min-max normalization to increase pixel intensity, and data augmentation is employed to prevent data imbalance problems. Improved categorization is achieved by the ResNet50 by extracting multi-scale characteristics from the images. Based on the simulation findings, it is evident that DenseNet201, which incorporates ResNet50 features, outperforms VGG16 and InceptionV3 due to the intricate interactions between data that dense connectivity enables. Furthermore, ResNet50-DenseNet201 performs better than FFNN-ResNet50 and HPC. In comparison to FFNN-ResNet50 and HPC, ResNet50-DenseNet201 has a high accuracy of 99.90%.

This paper presents an approach for semantic place categorization using data obtained from RGB cameras. Previous studies on visual place recognition and classification have shown that, by considering features derived from pre-trained Convolutional Neural Networks (CNNs) in combination with part-based classification models, high recognition accuracy can be achieved, even in presence of occlusions and severe viewpoint changes. Inspired by these works, we propose to exploit local deep representations, representing images as set of regions applying a Na\"{i}ve Bayes Nearest Neighbor (NBNN) model for image classification. As opposed to previous methods where CNNs are merely used as feature extractors, our approach seamlessly integrates the NBNN model into a fully-convolutional neural network. Experimental results show that the proposed algorithm outperforms previous methods based on pre-trained CNN models and that, when employed in challenging robot place recognition tasks, it is robust to occlusions, environmental and sensor changes.

Rotated target recognition is a challenge for Convolutional Neural Networks (CNN), and the current solution is to make CNN rotational invariant through data augmentation. However, data augmentation makes CNN easy to overfit small scale sonar image datasets, and increases its numbers of parameters and training time. This paper proposes to recognize rotated targets of sonar images using a novel CNN with Rotated Inputs (RICNN), which doesn’t need data augmentation. During training, RICNN was trained with sonar images of targets only at one orientation, which avoid it to learn multiple rotated versions of the same targets, and reduces both number of parameters and training time of CNN. During testing, RICNN calculated classification scores for each test image and its all-possible rotated versions. The max of these classification scores were used to simultaneously estimate the category and orientation of each target. Besides, to improve the generalization of RICNN on imbalanced sonar datasets, this paper also designs an imbalanced data sampler. Experiments on a self-made small, imbalanced sonar image rotated target recognition dataset show that the improved RICNN achieves 4.25% higher classification accuracy than data augmentation, and reduces the number of parameters and training time to 2.25% and 19.2% of that of data augmentation method. Moreover, RICNN achieves comparable orientation estimation accuracy with a CNN orientation regressor trained with data augmentation. Codes, dataset are publicly available.

Introduction: This paper presents a novel approach for classifying different types of steel rods using a combination of pre-trained convolutional neural networks (CNNs) and fuzzy logic systems. We utilize VGG16, ResNet50, and InceptionV3 models to extract features from grayscale images of steel rods, which are then classified using Support Vector Machines (SVM). Furthermore, we incorporate a fuzzy logic system to improve the final classification accuracy by combining the individual accuracies of each CNN model. Experimental results demonstrate the effectiveness of our approach in achieving high classification accuracy compared to traditional methods. Objectives: This research presents a novel multi-model approach for steel rod classification utilizing three distinct fuzzy logic systems: Fuzzy Recurrent Neural Network (RNN), Fuzzy Convolutional Neural Network (CNN), and Fuzzy Neural Network. The proposed system addresses the critical challenge of accurate defect detection and classification in steel manufacturing processes. By combining these complementary approaches, we achieve enhanced feature extraction capabilities and improved classification accuracy. Experimental results demonstrate that our multi-model system achieves a classification accuracy of 97.8%, surpassing single-model approaches by an average of 8.2%. The system successfully identifies surface defects, structural anomalies, and dimensional variations with high precision, making it suitable for real-time industrial applications. Methods: This paper introduces a novel approach for the feature extraction and classification of steel rods by integrating multiple models and utilizing fuzzy logic systems. The methodology leverages pre-trained convolutional neural networks (CNNs) for extracting comprehensive feature representations from steel rod images. These extracted features are then combined and integrated using fuzzy logic to enhance classification accuracy and reliability. By addressing the limitations of traditional manual inspection methods and single-model approaches, this integrated multi-model system offers a robust solution for real-time industrial implementation. The proposed system aims to improve the efficiency and effectiveness of quality control processes in steel rod manufacturing, ensuring consistent and accurate classification even under varying conditions and complexities. This innovative approach paves the way for more advanced and adaptable automated inspection systems in the industry. Results: The paper aims to develop an integrated multi-model system that combines three distinct fuzzy logic approaches to enhance feature extraction capabilities through complementary methodologies. By improving the classification accuracy and reliability, the system addresses the limitations of existing methods. The goal is to create a robust and adaptable system that is suitable for real-time industrial implementation for steel rod analysis and classification. This comprehensive approach leverages the strengths of multiple models and fuzzy logic techniques, ensuring accurate and efficient classification processes, even under complex and varying conditions. The proposed multi model system is designed to meet the stringent quality control requirements of industrial applications, ultimately improving productivity and product quality through reliable automated inspection and classification for steel rod with accuracy of 97.80%, precision of 97.30% and recall 97.50%. Conclusions: This paper presents a novel approach for the classification of steel rods using a combination of pre-trained CNN models and fuzzy logic systems. The experimental results demonstrate the effectiveness of our method in achieving high classification accuracy. This research demonstrates the effectiveness of a multi-model fuzzy logic approach for steel rod classification. The system achieves significant improvements over single-model approaches while maintaining real-time processing capabilities. Our approach provides a foundation for further research and development in automated industrial image classification.

This paper proposes a new method, called multilayer stacked covariance pooling (MSCP), for remote sensing scene classification. The innovative contribution of the proposed method is that it is able to naturally combine multilayer feature maps, obtained by pretrained convolutional neural network (CNN) models. Specifically, the proposed MSCP-based classification framework consists of the following three steps. First, a pretrained CNN model is used to extract multilayer feature maps. Then, the feature maps are stacked together, and a covariance matrix is calculated for the stacked features. Each entry of the resulting covariance matrix stands for the covariance of two different feature maps, which provides a natural and innovative way to exploit the complementary information provided by feature maps coming from different layers. Finally, the extracted covariance matrices are used as features for classification by a support vector machine. The experimental results, conducted on three challenging data sets, demonstrate that the proposed MSCP method can not only consistently outperform the corresponding single-layer model but also achieve better classification performance than other pretrained CNN-based scene classification methods.